Method and apparatus for improved presentation of information

ABSTRACT

A method and apparatus comprising generating a dynamic personalized webpage is disclosed. At least two webpages are loaded in a fashion that is hidden from the user. Content from the at least two webpages is extracted based on classification “of interest” by an artificial intelligence algorithm. A dynamic personalized webpage comprising extracted content is then generated and displayed to the user. In the preferred embodiment, the user&#39;s dynamic personalized webpage will be filled with advertisements tailored to the user and the user would receive at least some revenue from advertisements.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a continuation in part of U.S. Ser. No. 17/187,804filed on Feb. 28, 2021, which is a continuation in part of U.S. Ser. No.17/138,821 filed on Dec. 30, 2020.

TECHNICAL FIELD

Aspects of this disclosure are generally related to presentation ofinformation to a user.

BACKGROUND

Users work with a variety of websites and applications on mobile phones,tablets and laptop computers.

SUMMARY

All examples, aspects and features mentioned in this document can becombined in any technically possible way.

A method and apparatus comprising improving the presentation ofinformation to a user is disclosed. This is accomplished through an appor webpage, which can be called my site. This method involves segmentingpresented information on display into discrete items. An artificialintelligence (AI) algorithm categorizes the items. For example, thecategories could be “of interest” and “of non-interest” to a user.Passive or active feedback is used to train the AI algorithm and overtime. Thus, the AI learns which items are of interest and which itemsare of non-interest and over time, the personalized app can present morerelevant and personalized information. There are two improvements overthe current processes.

First, the data is presented to the user in a consistent format. If auser wants to look at the FoxNews.com webpage, he/she will have to learnhow to navigate through this page. If a user wants to look at theCNN.com webpage, he/she will have to learn how to navigate through thispage. Learning how to navigate a number of different webpages takes timeand energy.

Second, much of the data on a webpage is of non-interest to a givenuser. For example, FoxNews.com may have a featured article that thestock market has dropped 7% in a single trading day, but that piece ofinformation may be of non-interest to a particular user. Additionally, awebsite may be filled with unwanted advertisements.

The personalized webpage overcomes both of these difficulties bypresenting the information in a consistent format tailored to anindividual user. For example, a user can select features including, butnot limited to, the following: font size; layout of webpage; and,background of webpage. Additionally, the personalized webpage displaysitems of interest only. Specifically, items of non-interest arefiltered. Thus, the personalized webpage improves presentation ofinformation.

In some embodiments, items of non-interest are covered by a dynamiccontent blocking image. In other embodiments, items are filtered and anew webpage is generated, which looks similar to the parent webpage, butitems are resized and moved (e.g., in position or in orientation). Stillin other embodiments, a composite, personalized webpage with contentextracted from at least two sources is performed. Such personalizedwebpage can be designed to satisfy the user. Still in other embodiments,personalized secondary notifications (e.g., sounds, visual notificationssuch as digital objects, and haptic feedback) can supplement the newlydisplayed items on the personalized webpage. Still in other embodiments,AI filtering of applications can be performed, so as to limited theinformation overload, and display the “of interest” items to the user onthe personalized my site application/ website. Finally, the my siteapplication will have an AI driven psychological boost feature byassigning characteristics to a AI person who can serve many roles:friend; coach; counselor; teacher; or, even a grandmother of the user.

Some embodiments comprise generating a dynamic personalized webpagecomprising: using a first webpage wherein said first webpage is loaded,and wherein said first webpage is not displayed to a user; using asecond webpage wherein said second webpage is loaded, and wherein saidsecond webpage is not displayed to said user; using a computer algorithmto label a first item from said first webpage as “of interest”;extracting said first item from said first webpage; using said computeralgorithm to label a second item from said second webpage as “ofinterest”; extracting said second item from said second webpage;generating said dynamic personalized webpage wherein said dynamicpersonalized webpage comprises said first item and said second item; anddisplaying said dynamic personalized webpage to said user.

Some embodiments comprise wherein said computer algorithm comprises anartificial intelligence algorithm; and wherein said artificialintelligence algorithm learns based on feedback from said user. Someembodiments comprise wherein said artificial intelligence algorithmimproves classification accuracy of “of interest” based on at least oneof the group consisting of: an amount of time spent by said user on saidfirst item or said second item; whether said user clicks on said firstitem or said second item; and whether said user sends said first item orsaid second item to another person. Some embodiments comprise using athird webpage wherein said first webpage is loaded, an wherein saidfirst webpage is not displayed to a user; using a fourth webpage whereinsaid fourth webpage is loaded, and wherein said fourth webpage is notdisplayed to said user; using said computer algorithm to label a thirditem from said third webpage as “of interest”; extracting said thirditem from said third webpage; using said computer algorithm to label afourth item from said fourth webpage as “of interest”; extracting saidfourth item from said fourth webpage; and generating a second dynamicpersonalized webpage wherein said second dynamic personalized webpagecomprises said third item and said fourth item. Some embodimentscomprise wherein said first webpage and said second webpage are assignedto a first category; and wherein a third webpage and a fourth webpageare assigned to a second category. Some embodiments comprise when saiduser clicks on a button corresponding to said first category, saiddynamic personalized webpage comprising said first item and said seconditem is displayed to said user; and when said user clicks on a buttoncorresponding to said second category, said dynamic personalized webpagecomprising said third item and said fourth item is displayed to saiduser. Some embodiments comprise wherein said dynamic personalizedwebpage comprises advertisements. Some embodiments comprise wherein atleast some revenue from advertisers is paid to said user. Someembodiments comprise updating said dynamic personalized webpagethroughout based on updates from said first webpage and said secondwebpage. Some embodiments comprise wherein said first item comprises afirst hyperlink; wherein when said user clicks on said first hyperlink,a webpage corresponding to said first hyperlink is loaded and saidwebpage corresponding to said first hyperlink is not displayed to saiduser; using a computer algorithm to label an item from said webpagecorresponding to said first hyperlink as “of interest”; extracting saiditem from said webpage corresponding to said first hyperlink; modifyingsaid dynamic personalized webpage by incorporating said item from saidwebpage corresponding to said first hyperlink; and displaying saidmodified dynamic personalized webpage to said user. Some embodimentscomprise displaying a predetermined notification to said user based on aclassification of said content wherein said predetermined notificationis a sound notification. Some embodiments comprise displaying apredetermined notification to said user based on a classification ofsaid content wherein said predetermined notification is a hapticnotification. Some embodiments comprise displaying a predeterminednotification to said user based on a classification of said contentwherein said predetermined notification is a visual notification. Someembodiments comprise using a personalized display for said dynamicpersonalized webpage comprising changing a font setting of said firstwebpage or said second webpage to a user preferred font setting. Someembodiments comprise wherein said font setting comprises at least one ofthe group consisting of: a font type; a font size; and a font color.Some embodiments comprise using a personalized display for said dynamicpersonalized webpage comprising changing image size of said firstwebpage or said second webpage to a user preferred image size.

Some embodiments comprise a computer system comprising: a memory; aprocessor; a display; a communications interface; an interconnectionmechanism coupling the memory, the processor, the display and thecommunications interface; and wherein the memory is encoded with anapplication to generate a dynamic personalized webpage. Some embodimentscomprise a non-transitory computer readable medium having computerreadable code thereon for generating a dynamic personalized webpage.

Still other embodiments include a computerized device, configured toprocess all the method operations disclosed herein as embodiments of theinvention. In such embodiments, the computerized device includes amemory system, a processor, communications interface in aninterconnection mechanism connecting these components. The memory systemis encoded with a process that provides steps explained herein that whenperformed (e.g. when executing) on the processor, operates as explainedherein within the computerized device to perform all of the methodembodiments and operations explained herein as embodiments of theinvention. Thus any computerized device that performs or is programmedto perform processing explained herein is an embodiment of theinvention.

Other arrangements of embodiments of the invention that are disclosedherein include Software programs to perform the method embodiment stepsand operations Summarized above and disclosed in detail below. Moreparticularly, a computer program product is one embodiment that has acomputer-readable medium including computer program logic encodedthereon that when performed in a computerized device provides associatedoperations providing steps as explained herein.

The computer program logic, when executed on at least one processor witha computing system, causes the processor to perform the operations(e.g., the methods) indicated herein as embodiments of the invention.Such arrangements of the invention are typically provided as Software,code and/or other data structures arranged or encoded on a computerreadable medium such as an optical medium (e.g., CD-ROM), floppy or harddisk or other a medium such as firmware or microcode in one or more ROMor RAM or PROM chips or as an Application Specific Integrated Circuit(ASIC) or as downloadable software images in one or more modules, sharedlibraries, etc. The software or firmware or other Such configurationscan be installed onto a computerized device to cause one or moreprocessors in the computerized device to perform the techniquesexplained herein as embodiments of the invention. Software processesthat operate in a collection of computerized devices, such as in a groupof data communications devices or other entities can also provide thesystem of the invention. The system of the invention can be distributedbetween many software processes on several data communications devices,or all processes could run on a small set of dedicated computers, or onone computer alone.

It is to be understood that the embodiments of the invention can beembodied strictly as a software program, as Software and hardware, or ashardware and/or circuitry alone. Such as within a data communicationsdevice. The features of the invention, as explained herein, may beemployed in data processing devices and/or Software systems for Suchdevices. Note that each of the different features, techniques,configurations, etc. discussed in this disclosure can be executedindependently or in combination. Accordingly, the present invention canbe embodied and viewed in many different ways. Also, note that thisSummary section herein does not specify every embodiment and/orincrementally novel aspect of the present disclosure or claimedinvention. Instead, this Summary only provides a preliminary discussionof different embodiments and corresponding points of novelty overconventional techniques. For additional details, elements, and/orpossible perspectives (permutations) of the invention, the reader isdirected to the Detailed Description section and corresponding figuresof the present disclosure as further discussed below.

BRIEF DESCRIPTION OF THE FIGURES

The flow diagrams do not depict the syntax of any particular programminglanguage. Rather, the flow diagrams illustrate the functionalinformation one of ordinary skill in the art requires to fabricatecircuits or to generate computer software to perform the processingrequired in accordance with the present invention. It should be notedthat many routine program elements, such as initialization of loops andvariables and the use of temporary variables, are not shown. It will beappreciated by those of ordinary skill in the art that unless otherwiseindicated herein, the particular sequence of steps described isillustrative only and can be varied without departing from the spirit ofthe invention. Thus, unless otherwise stated the steps described beloware unordered meaning that, when possible, the steps can be performed inany convenient or desirable order.

The foregoing will be apparent from the following more particulardescription of preferred embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention.

FIG. 1A illustrates prior art comprising an image displayed on acomputer monitor at a first time point.

FIG. 1B illustrates prior art comprising an image displayed on acomputer monitor at a second time point.

FIG. 2 illustrates the my site application overview.

FIG. 3 illustrates an example homepage for the my siteapplication/website home page.

FIG. 4 illustrates an overview of the methodology of the dynamic contentblocker.

FIG. 5 illustrates types of data used to train the AI algorithm.

FIG. 6 illustrates types of categories for classification.

FIG. 7 illustrates segmentation of the image displayed on a computermonitor.

FIG. 8 illustrates using AI to determine which items to be displayed onmy site.

FIG. 9A illustrates a first time point of an image on a monitor whereina segmented item of said image is classified as non-interest.

FIG. 9B illustrates a dynamic content blocker to be applied to the imageat a first time point.

FIG. 9C illustrates a modified image at the first time point wherein anitem on the image classified as non-interest is blocked by the dynamiccontent blocker.

FIG. 9D illustrates a second time point of an image on a monitor whereina segmented item of said image is classified as non-interest.

FIG. 9E illustrates a dynamic content blocker to be applied to the imageat a second time point.

FIG. 9F illustrates a modified image at the second time point wherein anitem on the image classified as non-interest is blocked by the dynamiccontent blocker.

FIG. 10A illustrates a image on a monitor wherein a segmented item ofsaid image is classified as non-interest.

FIG. 10B illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a white appearance.

FIG. 10C illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a gray appearance.

FIG. 10D illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a gray appearance with slight transparency.

FIG. 10E illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a gray appearance with high transparency.

FIG. 10F illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has an appearance to match the webpage'sbackground.

FIG. 11 illustrates an overview of the generating a personalized webpageconsisting of re-arranging and resized “of interest” items only.

FIG. 12A illustrates segmentation of the image displayed on a computermonitor.

FIG. 12B illustrates an example of the personalized webpage withfiltering, re-arranging and resizing of components on the image.

FIG. 13 illustrates the generation of an AI generated composite image ofat least two webpages.

FIG. 14A illustrates a set of topics and webpages that the AI algorithmhas learned that are “of interest” to user 1.

FIG. 14B illustrates a set of topics and webpages that the AI algorithmhas learned that are “of interest” to user #2.

FIG. 14C illustrates a set of topics and webpages that the AI algorithmhas learned that are “of interest” to user #3.

FIG. 15A illustrates a standard webpage, which in this example isFoxNews.com.

FIG. 15B illustrates a standard webpage, which in this example isCNN.com.

FIG. 15C illustrates a user-specific, AI generated composite webpage,which would correspond to User #1 in FIG. 14A.

FIG. 16 illustrates a process of modifying the personalized, compositewebpage generated by an AI algorithm when a user clicks on a link on thepersonalized composite webpage.

FIG. 17A illustrates the personal page, which in this situation is acomposite user webpage.

FIG. 17B illustrates the source of the link, which was activated on FIG.17A.

FIG. 17C illustrates the personalized, composite webpage, which in thissituation shows the article selected in FIG. 17A.

FIG. 18 illustrates secondary notifications relating informing userabout displayed content.

FIG. 19 illustrates a table, which illustrates how classified content isdetermined to be displayed to a user.

FIG. 20 illustrates examples of artificial intelligence filteredapplications.

FIG. 21 illustrates a filtered, AI-personalized search.

FIG. 22 illustrates a filtered, AI-personalized consolidation of emailaccounts.

FIG. 23 illustrates utilizing information to enable a psychologicalboost for a user.

FIG. 24 illustrates achieving a psychological boost via a grandmother.

FIG. 25 illustrates a method and apparatus for a dynamic personalizedwebpage.

FIG. 26 illustrates options for the dynamic personalized webpage.

DETAILED DESCRIPTION

Some aspects, features and implementations described herein may includemachines such as computers, electronic components, optical components,and processes such as computer-implemented steps. It will be apparent tothose of ordinary skill in the art that the computer-implemented stepsmay be stored as computer-executable instructions on a non-transitorycomputer-readable medium. Furthermore, it will be understood by those ofordinary skill in the art that the computer-executable instructions maybe executed on a variety of tangible processor devices. For ease ofexposition, not every step, device or component that may be part of acomputer or data storage system is described herein. Those of ordinaryskill in the art will recognize such steps, devices and components inview of the teachings of the present disclosure and the knowledgegenerally available to those of ordinary skill in the art. Thecorresponding machines and processes are therefore enabled and withinthe scope of the disclosure.

FIG. 1A illustrates prior art comprising an image displayed on acomputer monitor at a first time point. Note various applications openon the desktop at the bottom of the image. Note that there areadvertisements displayed on the webpage.

FIG. 1B illustrates prior art comprising an image displayed on acomputer monitor at a second time point. Note at this second time point,a different portion of the webpage is shown. Note that theadvertisements are located at different positions on the screen.

FIG. 2 illustrates the my site application overview. 200 illustrates themy site application, which has several different features. 201illustrates the feature of utilizing a dynamic content blocker. 202illustrates the feature of generating a personalized webpage consistingof re-arranging and resized “of interest” items only. Alternatively,items can be changed in orientation. 203 illustrates the feature ofutilizing a personalized composite webpage consisting of “of interest”items only. 204 illustrates the feature of utilizing personalizedsecondary notifications informing a user about the displayed content.205 illustrates the feature of utilizing AI-filtered applications. 206illustrates the feature of utilizing an AI driven psychological boost.

FIG. 3 illustrates an example homepage for the my siteapplication/website home page. This presentation of the my siteapplication can be determined by the user or by an AI algorithm. In thisexample, the light blue background represents the user's preference.There are no advertisements and the my site application is cleanappearing. An AI-filtered, personalized news can be utilized and shownin this home page 300. The novelty of the my site application is theperformance of AI filtering so that the user is presented only “ofinterest” items. 301 illustrates the home button of the application. 302illustrates the AI-filtered, personalized Internet search button. 303illustrates the AI-filtered, personalized news button. For example,items from FoxNews.com and CNN.com can be analyzed by the AI and only“of interest” items are presented to the user. 304 illustrates theAI-filtered, consolidated suggested activities button. A user may have40+ applications, many of which are suggesting activities to the user.This my site interface can use AI-filtering to generate suggestedactivities to the user. 305 illustrates the AI consolidated emailsbutton. A user may have several email accounts including Hotmail,Outlook, Yahoo and Gmail. Learning each interface can be difficult. Inthis embodiment, AI filtering and presentation in a format that is wellreceived by the user is performed. 306 illustrates the AI-friend button,which can serve to boost psychological health. 307 illustrates a humancontact button, for applications such as texting or calling. 308illustrates the CNN.com banner, which was deemed to be “of interest” bythe AI algorithm and therefore imported into the my site application.309 illustrates an article from CNN.com, which was deemed to be “ofinterest” by the AI algorithm and therefore imported into the my siteapplication. 310 illustrates the FoxNews.com banner, which was deemed tobe “of interest” by the AI algorithm and therefore imported into the mysite application. 311 illustrates an article from FoxNews.com, which wasdeemed to be “of interest” by the AI algorithm and therefore importedinto the my site application.

FIG. 4 illustrates an overview of the methodology of the dynamic contentblocker. The preferred embodiment is for a user's computer to run thedynamic content blocker app in the background and use the phone howeverhe/she sees fit. The application will filter (hide) some of the contentclassified to be of non-interest, which is useful by reducinginformation overload to the user. This will improve the user'sexperience. For example, some advertisements may raise stress or not beappealing to a user. These such advertisements can be covered up by thedynamic content blocker. 400 illustrates a time point (e.g., a firsttime point). 401 illustrates a processing block comprising analyzing theappearance of an image displayed on monitor(s). Software runs oncomputer and analyzes the appearance of the monitor(s). The monitorincludes, but is not limited to the following: a desktop computermonitor; a laptop monitor; a TV; a smart phone; and, an extended realitydisplays (e.g., HoloLens 2). In some embodiments, it is envisioned thatitems of non-interest will be displayed as virtual objects within a 3Dworld. Items of non-interest in this embodiment could be filteredthrough processes taught in this patent. 402 illustrates a processingblock comprising segmenting the image into items. 403 illustrates aprocessing block comprising classifying each item. For example,implement an artificial intelligence algorithm, which analyzes thecontent of each item. The artificial intelligence algorithm can learnfrom feedback from a user. For example, the user can input a hot key anddraw a box over a displayed item of non-interest, so that it is hidden.This classification can be inputted into a training dataset, which isused to train the AI algorithm. The content can be the appearance of apicture, words or symbols within the segmented item. The items can beclassified as “of interest” or “of non-interest”. A certainty level canalso be implemented, which can be used to drive the appearance of thedynamic content blocker. For example, if an item is classified asgreater than or equal to 90% certainty that the item is “ofnon-interest”, then a dynamic content blocker can be generated for thisitem. If, however, the item is classified as less than or equal to 89%certainty that the item is “of non-interest”, then the dynamic contentblocker is not generated for this item. 404 illustrates a processingblock comprising for items classified as non-interest, generate acustomized dynamic content blocker to cover the items classified asnon-interest. 405 illustrates a processing block comprising display thedynamic content blocker on the monitor(s). The appearance is discussedsubsequently in this patent. 406 illustrates a subsequent time point. Atthe subsequent time point, return to processing block 401.

FIG. 5 illustrates types of data used to train the AI algorithm. Thegoal of this system is to optimize presentation of information to auser. To achieve this, it is absolutely essential to block (or filter)items of non-interest. It is also important to identify information thatis predicted to be of interest to the user and make it noticeable by theuser. Additionally, it is important to collect information from multiplesources. Custom pages are developed to maximize user appreciation.Finally, it is critical to have appropriate feedback to the AIalgorithm. The goal of the feedback it to help improve classificationinto the “of interest” category and the “of non-interest” category. Toachieve appropriate feedback, the user input must be classified alongwith the content.

With respect to user feedback, there are two types taught herein. Firstis active feedback from the user. This is where the user deliberatelydoes something to train the AI. For example, the user clicks on asegmented item (with option to generate manual input of classification)to deliberately train the AI. This could be done via an “of interest”button or an “of non-interest” button. This could also be done via a“classification button” where the user assigns a classification to teachthe AI why they classified it the way they did. For example,classifications buttons include, but are not limited to, the following:author; content; aesthetics of the image; time of day; and, others.

Second is passive feedback from user. This is where a camera canevaluate the user's facial expressions and the user's eye tracking. Thiscan reveal gaze direction. For example, if the user studies an imagewith a pleasant appearance on the face, that can be used as an indicatorof the “of interest” category. This could also be used to enhancepsychological therapy. For example, if a particular sound or image wererealized by eye tracking and facial expression analysis together toreduce stress, then these could be delivered in a strategic fashion. Forexample, if the person was yelling or stressed out, then the therapeuticimages and sound could kick in. Additional passive feedback includestime spent reading the article. If the user reads the full articlecarefully, this can be an indicator of the “of interest” category.Additionally, whether the article was forwarded to a friend can alsoserve as passive feedback from the user and be used to train the AIalgorithm. The AI algorithms could have personalities and voices. Andthere could be multiple. For example, a user could have 5 AI friends andsay “Titan, show me what is going on”. Titan is an AI algorithm whospecializes in finances pertinent to the user and would displaypertinent financial news. The user could say “Joey, I am feeling a bitstressed”. Joey is an AI algorithm who loves puppies and wants to sharevideos and has a nice soothing voice. The user could say “Jack, I know Ineed to work out, but I don't have the motivation.” Jack is a coach andwould show the latest athletic news. The user could say “Katie, what isit going to be like outside today.” Katie is an AI algorithm who lovesthe weather and brings up the weather and traffic news. The user couldsay “Olie, what is going on in the world”. Olie is an AI algorithm thatspecializes in world news of interest to the user and the “of interest”world news can be populated on the app.

Additionally, the AI algorithm will need to classify the content tooptimize the experience for the user. The content can be classified byimage analysis. For example, the AI algorithm could learn that the userfinds certain types of images “of interest” and other types of images“of non-interest”. Other classification categories could also beutilized, which is discussed in image ##. The classification of thecontent includes, but is not limited to the following: image analysis;symbol analysis; semantic analysis; sound analysis; emoji analysis; and,text appearance analysis. For each group a variety of sub-groups can beused. For example, for semantic analysis, the word “mask” may be usedfor AI analysis. If the word “mask” is detected (e.g., through opticalcharacter recognition), then the article could be classified as COVIDand delivered to User #1 in FIG. 14A.

FIG. 6 illustrates types of categories for classification. A range ofcategories can be used. For example, the user could use their app tosearch by category. For example, if the user wanted to read news just tosee what is interesting, the user could search by the “of interestcategory” with the option to have AI determine which websites to look ator the option to check CNN and FoxNews and have the AI search from thereand generate a composite webpage. In addition to the “of interest”search, the user could also perform the “of importance” search, the“makes happier” search, the “motivates” search, the “likely to be ofinterest to girlfriend” and the likely to be on bucket list” app. Binaryclassifications are shown to teach this concept. However, differentvariable types could also be used. For example, categorical variables,such as mild, moderate, severe.

FIG. 7 illustrates segmentation of the image displayed on a computermonitor. A person who is of ordinary skill in the art could perform avariety of segmentation algorithms. 700 illustrates the image displayedon a computer monitor. 701 illustrates a first segmented item in theimage 700. 702 illustrates a second segmented item in the image 700. 703illustrates a third segmented item in the image 700. 704 illustrates afourth segmented item in the image 700. 705 illustrates a fifthsegmented item in the image 700. 706 illustrates a sixth segmented itemin the image 700. 707 illustrates a seventh segmented item in the image700. 708 illustrates the eighth segmented item in the image 700.

FIG. 8 illustrates using AI to determine which items to be displayed onmy site. Assume that the threshold is set at 80%. Therefore, if an itemhas an 80% chance or higher (predicted by AI) to be “of interest” to theuser, it will be displayed on the my site application. If an item haslower than an 80% chance (predicted by AI) to be “of interest” to theuser, then it will not be displayed on the my site application. 800illustrates an image. 801 illustrates a first segmented item, which wasdetermined by the AI algorithm to have a probability of being “ofinterest” of 7%. Therefore, item 801 is not selected for display on themy site app. 802 illustrates a second segmented item, which wasdetermined by the AI algorithm to have a probability of being “ofinterest” of 79%. Therefore, item 802 is not selected for display on themy site app. 803 illustrates a third segmented item, which wasdetermined by the AI algorithm to have a probability of being “ofinterest” of 81%. Therefore, item 803 is selected for display on the mysite app. 804 illustrates a fourth segmented item, which was determinedby the AI algorithm to have a probability of being “of interest” of 31%.Therefore, item 804 is not selected for display on the my site app. 805illustrates a fifth segmented item, which was determined by the AIalgorithm to have a probability of being “of interest” of 99%.Therefore, item 805 is selected for display on the my site app.

FIG. 9A illustrates a first time point of an image on a monitor whereina segmented item of said image is classified as non-interest. 900illustrates the image on the monitor. 901 illustrates an item in theimage, which is classified as non-interest.

FIG. 9B illustrates a dynamic content blocker to be applied to the imageat a first time point. 902 illustrates the border of the image on themonitor. 903 illustrates the dynamic content blocker to be applied tothe image at the first time point.

FIG. 9C illustrates a modified image at the first time point wherein anitem on the image classified as non-interest is blocked by the dynamiccontent blocker. 904 illustrates the modified image at the first timepoint wherein an item classified on the image as non-interest is blockedby the dynamic content blocker. 905 illustrates a black rectangle andthe item is hidden from visibility.

FIG. 9D illustrates a second time point of an image on a monitor whereina segmented item of said image is classified as non-interest. 906illustrates the image on the monitor. 907 illustrates an item in theimage, which is classified as non-interest. Note that this item (adifferent advertisement) is different from the item at the first timepoint.

FIG. 9E illustrates a dynamic content blocker to be applied to the imageat a second time point. 908 illustrates the border of the image on themonitor. 909 illustrates the dynamic content blocker to be applied tothe image at the second time point. Note that the dynamic contentblocker has change in shape, size and location as compared to FIG. 9B.Also note that it can be change in appearance.

FIG. 9F illustrates a modified image at the second time point wherein anitem on the image classified as non-interest is blocked by the dynamiccontent blocker. 910 illustrates the modified image at the second timepoint wherein an item classified on the image as non-interest is blockedby the dynamic content blocker. 911 illustrates a black rectangle andthe item is hidden from visibility. Additionally, the links behind thedynamic content blocker could be inaccessible.

FIG. 10A illustrates a first time point of an image on a monitor whereina segmented item of said image is classified as non-interest. 1000illustrates the image on the monitor. 1001 illustrates an item in theimage, which is segmented and classified as non-interest.

FIG. 10B illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a white appearance. 1002 illustrates amodified image on the monitor, which is modified as compared to FIG.10A. 1003 illustrates a dynamic content blocker, which has a whiteappearance, which hides the item of non-interest.

FIG. 10C illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a gray appearance. 1004 illustrates amodified image on the monitor, which is modified as compared to FIG.10A. 1005 illustrates a dynamic content blocker, which has a gray,non-transparent appearance, which hides the item of non-interest.

FIG. 10D illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a gray appearance with slight transparency.1006 illustrates a modified image on the monitor, which is modified ascompared to FIG. 10A. 1007 illustrates a dynamic content blocker, whichhas a gray, slightly transparent appearance, which hides the item ofnon-interest.

FIG. 10E illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has a gray appearance with high transparency.1008 illustrates a modified image on the monitor, which is modified ascompared to FIG. 10A. 1009 illustrates a dynamic content blocker, whichhas a gray, highly transparent appearance, which hides the item ofnon-interest.

FIG. 10F illustrates the image in FIG. 10A with monitor a dynamiccontent blocker, which has an appearance to match the webpage'sbackground. 1010 illustrates a modified image on the monitor, which ismodified as compared to FIG. 10A. 1011 illustrates a dynamic contentblocker, which has a light gray appearance to match the webpage'sbackground, which hides the item of non-interest. The example shown isof a 2D image on a 2D monitor. A wide range of image processingtechniques can be used in conjunction with the dynamic content blocker.These techniques are discussed in the following patents and patentapplication disclosed herein.

These same taught techniques can also be applied to 3D images asdisplayed on head display units, which provide stereoscopic viewing onan extended reality display unit. This is described in U.S. Pat. No.8,384,771, METHOD AND APPARATUS FOR THREE DIMENSIONAL VIEWING OF IMAGES,which is incorporated by reference in its entirety. This patent teachesimage processing techniques including volume generation, filtering,rotation, and zooming.

In some embodiments, stereoscopic viewing of the virtual 3D mannequin isperformed with convergence, which is described in U.S. Pat. No.9,349,183, METHOD AND APPARATUS FOR THREE DIMENSIONAL VIEWING OF IMAGES,which is incorporated by reference in its entirety. This patent teachesshifting of convergence. This feature can be used in combination withfiltering.

In some embodiments, stereoscopic viewing can be performed using adisplay unit, which incorporates polarized lenses, which is described inU.S. Pat. No. 9,473,766, METHOD AND APPARATUS FOR THREE DIMENSIONALVIEWING OF IMAGES, which is incorporated by reference in its entirety.

In some embodiments, advancements to display units can be incorporatedfor viewing the virtual 3D mannequin, which are taught in U.S. patentapplication Ser. No. 16/828,352, SMART GLASSES SYSTEM and U.S. patentapplication Ser. No. 16/997,830, ADVANCED HEAD DISPLAY UNIT FOR FIREFIGHTERS, which are both incorporated by reference in their entirety.

In some embodiments, advancements in display units are taught in U.S.patent application Ser. No. 17/120,109, ENHANCED VOLUME VIEWING, whichis incorporated by reference in its entirety. Included herein is a headdisplay unit, which is improved by incorporating geo-registration.

Some embodiments comprise utilizing an improved field of view on anextended reality head display unit, which is taught in U.S. patentapplication Ser. No. 16/893,291, A METHOD AND APPARATUS FOR A HEADDISPLAY UNIT WITH A MOVABLE HIGH RESOLUTION FIELD OF VIEW, which isincorporated by reference in its entirety.

In some embodiments, image processing steps can be performed using a 3Dvolume cursor, which is taught in U.S. Pat. No. 9,980,691, METHOD ANDAPPARATUS FOR THREE DIMENSIONAL VIEWING OF IMAGES, and U.S. Pat. No.10,795,457, INTERACTIVE 3D CURSOR, both of which are incorporated byreference in its entirety.

In some embodiments, a precision sub-volume can be utilized inconjunction with the virtual 3D mannequin, which is taught in U.S.patent application Ser. No. 16/927,886, A METHOD AND APPARATUS FORGENERATING A PRECISION SUB-VOLUME WITHIN THREE-DIMENSIONAL IMAGEDATASETS, which is incorporated by reference in its entirety.

In some embodiments, viewing of a structure at two different time pointscan be performed using a ghost imaging technique, which is taught inU.S. Pat. No. 10,864,043, INTERACTIVE PLACEMENT OF A 3D DIGITALREPRESENTATION OF A SURGICAL DEVICE OR ANATOMIC FEATURE INTO A 3DRADIOLOGIC IMAGE FOR PREOPERATIVE PLANNING, which is incorporated byreference in its entirety.

Some embodiments comprise selecting a specific surgical device forpre-operative planning, which is taught in U.S. patent application Ser.No. 17/093,322, A METHOD OF SELECTING A SPECIFIC SURGICAL DEVICE FORPREOPERATIVE PLANNING, which is incorporated by reference in itsentirety.

Some embodiments comprise those taught in U.S. patent application Ser.No. 16/867,102, METHOD AND APPARATUS OF CREATING A COMPUTER-GENERATEDPATIENT SPECIFIC IMAGE, which is incorporated by reference in itsentirety. Key techniques include using patient factors (e.g., history,physical examination findings, etc.) to generate a volume.

Some embodiments comprise those taught in U.S. Pat. No. 10,586,400,PROCESSING 3D MEDICAL IMAGES TO ENHANCE VISUALIZATION, and U.S. Pat. No.10,657,731, PROCESSING 3D MEDICAL IMAGES TO ENHANCE VISUALIZATION, bothof which are incorporated by reference in its entirety.

Some embodiments comprise performing deformation techniques so thatportions of the virtual 3D mannequin can be deformed and move inrelation to other portions of the virtual 3D mannequin, which is taughtin U.S. patent application Ser. No. 16/195,251, INTERACTIVE VOXELMANIPULATION IN VOLUMETRIC MEDICAL IMAGING FOR VIRTUAL MOTION,DEFORMABLE TISSUE, AND VIRTUAL RADIOLOGICAL DISSECTION, which isincorporated by reference in its entirety.

Some embodiments comprise those taught in U.S. patent application Ser.No. 16/736,731, RADIOLOGIST-ASSISTED MACHINE LEARNING WITH INTERACTIVE,VOLUME SUBTENDING 3D CURSOR, which is incorporated by reference in itsentirety.

Some embodiments comprise wherein at least some component of theinserted 3D dataset into the virtual 3D mannequin are derived fromcross-sectional imaging data fine-tuned with phantoms, which is taughtin U.S. patent application Ser. No. 16/752,691, IMPROVING IMAGE QUALITYBY INCORPORATING DATA UNIT ASSURANCE MARKERS, which is incorporated byreference in its entirety.

Some embodiments comprise utilizing halo-type segmentation techniques,which are taught in U.S. patent application Ser. No. 16/785,606,IMPROVING IMAGE PROCESSING VIA A MODIFIED SEGMENTED STRUCTURE, which isincorporated by reference in its entirety.

Some embodiments comprise using techniques for advanced analysis of thevirtual 3D mannequin taught in U.S. patent application Ser. No.16/939,192, RADIOLOGIST ASSISTED MACHINE LEARNING, which areincorporated by reference in its entirety.

Some embodiments comprise performing smart localization from a firstvirtual 3D mannequin to a second virtual 3D mannequin, such as in ananatomy lab, which is performed via techniques taught in U.S. patentapplication Ser. No. 17/100,902, METHOD AND APPARATUS FOR AN IMPROVEDLOCALIZER FOR 3D IMAGING, which is incorporated by reference in itsentirety.

Some embodiments comprise performing a first imaging examination with afirst level of mechanical compression and a second imaging examinationwith a second level of mechanical compression and analyzing differencestherein, which is taught in U.S. patent application Ser. No. 16/594,139,METHOD AND APPARATUS FOR PERFORMING 3D IMAGING EXAMINATIONS OF ASTRUCTURE UNDER DIFFERING CONFIGURATIONS AND ANALYZING MORPHOLOGICCHANGES, which is incorporated by reference in its entirety.

Some embodiments comprise display using an optimized image refresh rate,which is taught in U.S. patent application Ser. No. 16/842,631, A SMARTSCROLLING SYSTEM, which is incorporated by reference in its entirety.

Some embodiments comprise display using priority volume rendering, whichis taught in U.S. Pat. No. 10,776,989, A METHOD AND APPARATUS FORPRIORITIZED VOLUME RENDERING, which is incorporated by reference in itsentirety.

Some embodiments comprise display using tandem volume rendering, whichis taught in U.S. patent Ser. No. 17/033,892, A METHOD AND APPARATUS FORTANDEM VOLUME RENDERING, which is incorporated by reference in itsentirety.

Some embodiments comprise display using optimized fashion byincorporating eye tracking, which is taught in U.S. patent applicationSer. No. 16/936,293, IMPROVING VISUALIZATION OF IMAGES VIA AN ENHANCEDEYE TRACKING SYSTEM, which is incorporated by reference in its entirety.

Some embodiments comprise enhancing collaboration for analysis byincorporating teachings from U.S. patent application Ser. No.17/072,350, OPTIMIZED IMAGING CONSULTING PROCESS FOR RARE IMAGINGFINDINGS, which is incorporated by reference in its entirety.

Some embodiments comprise improving multi-user viewing of the virtual 3Dmannequin by incorporating teachings from U.S. patent application Ser.No. 17/079,479, AN IMPROVED MULTI-USER EXTENDED REALITY VIEWINGTECHNIQUE, which is incorporated by reference in its entirety.

Some embodiments comprise improving analysis of images through use ofgeo-registered tools, which is taught in U.S. Pat. No. 10,712,837, USINGGEO-REGISTERED TOOLS TO MANIPULATE THREE-DIMENSIONAL MEDICAL IMAGES,which is incorporated by reference in its entirety.

Some embodiments comprise integration of virtual tools withgeo-registered tools, which is taught in U.S. patent application Ser.No. 16/893,291, A METHOD AND APPARATUS FOR THE INTERACTION OF VIRTUALTOOLS AND GEO-REGISTERED TOOLS, which is incorporated by reference inits entirety.

Some embodiments comprise those taught in U.S. patent application Ser.No. 16/506,073, A METHOD FOR ILLUSTRATING DIRECTION OF BLOOD FLOW VIAPOINTERS, which is incorporated by reference in its entirety and U.S.Pat. No. 10,846,911, 3D IMAGING OF VIRTUAL FLUIDS AND VIRTUAL SOUNDS,which is also incorporated by reference in its entirety.

Some embodiments comprise those taught in U.S. patent Ser. No.17/075,799, OPTIMIZING ANALYSIS OF A 3D PRINTED OBJECT THROUGHINTEGRATION OF GEO-REGISTERED VIRTUAL OBJECTS, which is incorporated byreference in its entirety.

Some embodiments also involve a 3D virtual hand, which can begeo-registered to the virtual 3D mannequin. Techniques herein aredisclosed in U.S. patent application Ser. No. 17/113,062, A METHOD ANDAPPARATUS FOR A GEO-REGISTERED 3D VIRTUAL HAND, which is incorporated byreference in its entirety.

Some embodiments comprise those taught in U.S. patent application Ser.No. 16/654,047, METHOD TO MODIFY IMAGING PROTOCOLS IN REAL TIME THROUGHIMPLEMENTATION OF ARTIFICIAL, which is incorporated by reference in itsentirety.

Some embodiments comprise those taught in U.S. patent application Ser.No. 16/597,910, METHOD OF CREATING AN ARTIFICIAL INTELLIGENCE GENERATEDDIFFERENTIAL DIAGNOSIS AND MANAGEMENT RECOMMENDATION TOOL BOXES DURINGMEDICAL PERSONNEL ANALYSIS AND REPORTING, which is incorporated byreference in its entirety.

FIG. 11 illustrates an overview of the generating a personalized webpageconsisting of re-arranging and resized “of interest” items only.

The preferred embodiment is for a user to view only items of interest ona given webpage during a browsing session. This will reduce stress frominformation overload and cause a more pleasing browsing experience. 1100illustrates a time point (e.g., a first time point). 1101 illustrates aprocessing block comprising analyzing the appearance of a webpage (e.g.,FoxNews.com). Software runs on computer and analyzes the appearance ofthe monitor(s). The monitor includes, but is not limited to thefollowing: a desktop computer monitor; a laptop monitor; a TV; a smartphone; and, an extended reality displays (e.g., HoloLens 2). In someembodiments, it is envisioned that items of non-interest will bedisplayed as virtual objects within a 3D world. Items of non-interest inthis embodiment could be filtered through processes taught in thispatent. 1102 illustrates a processing block comprising segmenting theimage into items. 1103 illustrates a processing block comprisingclassifying each item. For example, implement an artificial intelligencealgorithm, which analyzes the content of each item. The artificialintelligence algorithm can learn from feedback from a user. For example,the user can input a “like” or a “dislike” on a segmented item that ispresented to the user by the AI algorithm. This classification can beinputted into a training dataset, which is used to train the AIalgorithm. The content can be the appearance of a picture, words orsymbols within the segmented item. The items can be classified as “ofinterest” or “of non-interest”. A certainty level can also beimplemented. For example, if an item is classified as greater than orequal to 90% certainty that the item is “of interest”, then item will bedisplayed on the personalized webpage. If this 90% threshold is not met,then the classified item will not be displayed on the personalizedwebpage. 1104 illustrates a processing block comprising generating apersonalized webpage containing only items “of interest” and notcontaining items “of non-interest.” 1105 illustrates a processing blockcomprising displaying the personalized webpage on the monitor(s). Theappearance is discussed subsequently in this patent. 1106 illustrates asubsequent time point. At the subsequent time point, return toprocessing block 1101.

FIG. 12A illustrates segmentation of the image displayed on a computermonitor. 1200 a illustrates a banner of FoxNews website. 1201 aillustrates a first segmented item in the image. 1202 a illustrates asecond segmented item in the image. 1203 a illustrates a third segmenteditem in the image. 1204 a illustrates a fourth segmented item in theimage. 1205 a illustrates a fifth segmented item in the image. 1206 aillustrates a sixth segmented item in the image. 1207 a illustrates aseventh segmented item in the image.

FIG. 12B illustrates an example of the personalized webpage withfiltering, re-arranging and resizing of components on the image. 1200 aillustrates a banner of FoxNews.com website, which is not re-sized orrearranged. Note that 1201 a, 1203 a and 1204 a have been filtered(subtracted or hidden) due to the AI algorithm determining that theseitems are of “non-interest”. Note that 1202 b illustrates the same imageas 1202 a in FIG. 12A, but it is now enlarged, and moved to a differentposition. Note that 1205 b illustrates the same image as 1205 a in FIG.12A, but it is now enlarged, and moved to a different position. Notethat 1206 b illustrates the same image as 1206 a in FIG. 12A, but it isnow enlarged, and moved to a different position. Note that 1207 billustrates the same image as 1207 a in FIG. 12A, but it is nowenlarged, and moved to a different position. Thus, it does not matterwhat visual items of non-interest Foxnews.com website displays, usingthis app will allow filtering and re-arranging to maximize theindividual user's experience.

FIG. 13 illustrates the generation of an AI generated composite image ofat least two webpages. 1300 illustrates a processing block comprisingdetermining an image format that is liked by a user. This could take theform of a dynamic personal webpage or an application. A variety ofscenes, pictures, sounds, colors, happy verses, or other features asdesired by the user. Alternatively, this image could also be blank for aclean feel. The image could be user generated by could be generated byan AI system. The image could change throughout the day. This softwarethat executes this program could be running in the cloud or on thecomputer. 1301 illustrates a processing block comprising determining(e.g., user input, by personalized AI) at least two webpages that maycontain items of interest for a user. For example, a user could go tothe main personalized webpage and then select (e.g., click on checkboxes) two or more webpages to browse. Alternatively, an AI algorithmcan learn via collected patterns on the user as training data whichwebsites the user typically views. For example, the user may regularlylook at the weather first thing in the morning and the headlines of thelocal news to look for traffic patterns. The AI would learn thisbehavior over time and the user would go to a main user-specific app andthe AI algorithm would already have learned what the user is prefers tobe viewing (through machine learning, such as deep learning algorithmsaccompanied by the training data). The types of information presented inthe composite image could be varied throughout the day. For example, oneof the hot topics that a person may be interested in reading uponarriving to work each day is the status of the COVID vaccines andstimulus bill. The user may be interested in reading these if and onlyif the hot topics are on the main page of the news source (e.g.,breaking news only). That would help the user's information be moresimilar to the general population. 1302 illustrates a processing blockcomprising determining (e.g., by using personalized AI) content from thefirst source (e.g., FoxNews webpage) that is predicted to be of interestfor a user. 1303 illustrates a processing block comprising determining(e.g., by using personalized AI) content from at least one additionalsource (e.g., CNN webpage) that is predicted to be of interest for auser. 1304 illustrates a processing block comprising extracting an itemor items from said first source (e.g., article on COVID vaccine). 1305illustrates a processing block comprising extracting an item from saidsecond source (e.g., article on stimulus package). 1306 illustrates aprocessing block comprising generating a user-specific, AI generatedcomposite webpage. 1307 illustrates a processing block comprisingpresenting the user-specific, AI generated composite image to the user.This could be from any type of display, such as a smart phone, laptop,tablet or extended reality display. 1308 illustrates monitoring for userfeedback (e.g., facial expression, reading the article) to train AIalgorithm.

FIG. 14A illustrates a set of topics and webpages that the AI algorithmhas learned that are “of interest” to user #1. With respect to topics,the AI algorithm has learned that “COVID” and “stimulus bill” articlesshould be classified as “of interest” for User #1. With respect towebpages, the AI algorithm has learned that the “CNN main page only” and“FoxNews main page only” articles should be classified as “of interest”for User #1. The AI algorithm will learn that these “of interest” itemscan be extracted and placed in a user-customized, personalized, dynamicwebpage. It should be noted that User #1's personalized, dynamic webpageis illustrated in FIG. 15C.

FIG. 14B illustrates a set of topics and webpages that the AI algorithmhas learned that are “of interest” to user #2. With respect to topics,the AI algorithm has learned that “Lung Cancer” articles should beclassified as “of interest” for User #2. With respect to webpages, theAI algorithm has learned that the entire “MSNBC” webpage, the entire“FoxNews” webpage and the entire “CNN” webpage articles should beclassified as “of interest” for User #2.

FIG. 14C illustrates a set of topics and webpages that the AI algorithmhas learned that are “of interest” to user #3. With respect to topics,the AI algorithm has learned that “vaccine passport” articles should beclassified as “of interest” for User #3. With respect to webpages, theAI algorithm has learned that the entire “MSNBC” webpage, the entire“FoxNews” webpage and the entire “CNN” webpage articles should beclassified as “of interest” for User #3. Thus, the AI algorithm willlearn that different users classify different items differently. In someembodiments, a user could share his/her profile to another user, so theycould get a idea of their friend's interests and associated newsprofile.

FIG. 15A illustrates a standard webpage, which in this example isFoxNews.com. 1500 illustrates a first segmented item on the FoxNews.comwebpage, which is the banner with multiple buttons for navigation. Thisis categorized as “of interest”. This can be done by a user because itis used for navigation on the composite personalized webpage.Alternatively, it can be done by an AI algorithm. 1501 illustrates asecond segmented item on the FoxNews.com webpage, which is anadvertisement for a 30-day free trial for a streaming. The AIcategorizes this as “of non-interest”. 1502 illustrates a thirdsegmented item on the FoxNews.com webpage, which is an link to “TheFive” show. The AI categorizes this as “of non-interest”. 1503illustrates a fourth segmented item on the FoxNews.com webpage, which isof Harris Faulkner hosting a special on COVID vaccines. The AIcategorizes this as “of interest”. The AI learns that the user seeks outnew information on COVID. 1504 illustrates a fifth segmented item on theFoxNews.com webpage, which is of Rep. Dade Phelan speaking on Texas. TheAI categorizes this as “of non-interest”. 1505 illustrates a sixthsegmented item on the FoxNews.com webpage, which is a link toFoxNation.com stream live. The AI categorizes this as “of non-interest”.1506 illustrates a seventh segmented item on the FoxNews.com webpage,which is a VRBO advertisement. The AI categorizes this as “ofnon-interest”.

FIG. 15B illustrates a standard webpage, which in this example isCNN.com. 1508 illustrates a first segmented item on the CNN.com webpage,which shows a video of a medical angiography room with a play buttondisplayed. The AI categorizes this as “of non-interest”. 1509illustrates a second segmented item on the CNN.com webpage, which showsan advertisement for the Cleveland Clinic. The AI categorizes this as“of non-interest”. 1510 illustrates a third segmented item on theCNN.com webpage, which shows two medical doctors wearing ClevelandClinic white coats. The AI categorizes this as “of non-interest”. 1511 aillustrates a fourth segmented item on the CNN.com webpage, which is thegeneral banner with multiple buttons for navigation. This is categorizedas “of interest”. This can be done by a user because it is used fornavigation on the composite personalized webpage. Alternatively, it canbe done by an AI algorithm. 1511 b illustrates a fifth segmented item onthe CNN.com webpage, which is the live updates banner with multiplebuttons for navigation. This is categorized as “of interest”. This canbe done by a user because it is used for navigation on the compositepersonalized webpage. Alternatively, it can be done by an AI algorithm.1512 illustrates a sixth segmented item on the CNN.com webpage, which isa featured article stating “Biden doesn't penalize Saudi Crown Prince”.The AI categorizes this as “of non-interest”. 1513 illustrates a seventhsegmented item on the CNN.com webpage, which is a link, which states“What you can expect from the $1.9 trillion stimulus”. The AIcategorizes this as “of interest”. 1514 illustrates a eighth segmenteditem on the CNN.com webpage, which is a link, which states “LIVE UPDATESHere's what action is expected today on the latest stimulus bill”. TheAI categorizes this as “of interest”. 1515 illustrates a ninth segmenteditem on the CNN.com webpage, which is a link which states “Analysis:$1.9 trillion relief bill is a shockwave”. The AI categorizes this as“of non-interest”.

1516 illustrates a tenth segmented item on the CNN.com webpage, which isa link which states “Senate Democrats look for other ways to boostminimum wage after suffering setback”. The AI categorizes this as “ofnon-interest”. 1517 illustrates an eleventh segmented item on theCNN.com webpage, which is a link which states “Senate Democrats look forother ways to boost minimum wage after suffering setback”. The AIcategorizes this as “of non-interest”. 1518 illustrates a twelfthsegmented item on the CNN.com webpage, which is a link, which statesthat “Rand Paul slammed for grilling Biden's transgender healthnominee”. The AI categorizes this as “of non-interest”. 1519 illustratesa thirteenth segmented item on the CNN.com webpage, which is a linkwhich states “Analysis: Biden sends a message to Iran, but with ascalpel instead of a sledgehammer”. The AI categorizes this as “ofnon-interest”. 1520 illustrates a fourteenth segmented item on theCNN.com webpage, which is a link which states “When will the US reachherd immunity?”. The AI categorizes this as “of interest”. 1521illustrates a fifteenth segmented item on the CNN.com webpage, which isan image stating “American Conservative Union”. The AI categorizes thisas “of non-interest”.

FIG. 15C illustrates a user-specific, AI generated composite webpage,which would correspond to User #1 in FIG. 14A. 1522 illustrates thelight blue background, which was user selected to be a soothing color.1523 illustrates the CNN banner, which allows the user navigation of theCNN site, but using the user-specific, AI generated composite webpage asan interface. This obviates the need to look at the CNN webpage and theFoxNews webpage and then sort through numerous articles of non-interest;therefore, it is useful. It should be noted that the whole webpages canbe analyzed and content classified as “of interest” or “ofnon-interest”, not just a portion of the webpage. Thus, this also servesas an advantage and improvement over the prior art. 1524 illustrates thefourteenth segmented item on the CNN.com webpage, as shown in 1520 inFIG. 15B, which is a link which states “When will the US reach herdimmunity?”. Since the AI categorized this as “of interest”, it isdisplayed on the user-specific, AI generated composite webpage. 1525illustrates the seventh segmented item on the CNN.com webpage, as shownin 1513 in FIG. 15B, which is a link which states “What you can expectfrom the $1.9 trillion stimulus”. Since the AI categorized this as “ofinterest”, it is displayed on the user-specific, AI generated compositewebpage. 1526 illustrates the eighth segmented item on the CNN.comwebpage, as shown in 1514 in FIG. 15B, which is a link which states“LIVE UPDATES Here's what action is expected today on the lateststimulus bill”. Since the AI categorized this as “of interest”, it isdisplayed on the user-specific, AI generated composite webpage. Sinceitems 1525 and 1526 are associated with one another, they are displayedtogether. 1527 illustrates the first segmented item on the FoxNews.comwebpage, as shown in 1500 in FIG. 15A, which is the banner with multiplebuttons for navigation. Since the AI categorized this as “of interest”,it is displayed on the user-specific, AI generated composite webpage.1528 illustrates the fourth segmented item on the FoxNews.com webpage,as shown in 1503 in FIG. 15A, which is of Harris Faulkner hosting aspecial on COVID vaccines. Since the AI categorized this as “ofinterest”, it is displayed on the user-specific, AI generated compositewebpage. In some embodiments, the user could sell space to advertisementagencies and get paid for having advertisement on their my siteapplication.

FIG. 16A illustrates a process of modifying the personalized, compositewebpage generated by an AI algorithm when a user clicks on a link on thepersonalized composite webpage. Processing block 1600 illustrates a userselects a link on composite page. In some embodiments, the AI couldperform the selection of the link on the composite page. Processingblock 1601 illustrates loading the source webpage, which corresponds tothe link. Processing block 1602 illustrates running the AI analysis onthe source webpage in processing block 1601 to determine items “ofinterest”. Processing block 1603 illustrates generating newpersonalized, composite webpage or modify existing personalizedcomposite page to include items of interest as determined by the AI inprocessing block 1602. Processing block 1604 illustrates displaying thenew composite page or existing composite page.

FIG. 17A illustrates the personal page, which in this situation is acomposite user webpage.

The personalized composite webpage, which contains the several itemsdeemed by the AI algorithm to be of interest to the user. In thisexample, item 1725 is an article titled “what you can expect from the$1.9 trillion stimulus”, which is clicked on by the user. The process inFIG. 14 is performed.

FIG. 17B illustrates the source of the link, which was activated on FIG.17A. In this patent, only items deemed by the AI algorithm deemed to be“of interest” are displayed to the user. The remaining items are notdisplayed to the user.

FIG. 17C illustrates the personalized, composite webpage, which in thissituation shows the article selected in FIG. 17A. Other content in thelink shown in FIG. 17B are not displayed in the personalized, compositewebpage. The process in FIG. 16 is performed. Thus, clicking on a linkon the personalized composite webpage displays only “of interest” itemsfrom the link.

FIG. 18 illustrates secondary notifications relating informing userabout displayed content. Processing block 1800 illustrates developpre-determined criteria wherein predetermined criteria associatescontent classifications with notifications (sound(s), haptic feedback,visual notification(s)). For example, content in an item is classifiedas of high interest to a financial investment in a personal stock of auser is associated with a predetermined notification is a “ka-ching”sound. Content in an item is classified as a exciting sports relatedcontent is associated with a predetermined flash of a picture of abaseball over the item. Content in an item is classified as an excitingevent that a user may want to participate in is associated with a hapticfeedback notification. Processing block 1801 illustrates classifyingcontent displayed on a monitor. Alternatively, classification of contenton a portion of a monitor could be performed (e.g., a webpage). In someembodiments, a digital symbol can be shown while the program is activeto indicate to the user that the image on the monitor is being analyzed,filtered and displayed with predetermined notifications. Processingblock 1802 illustrates if classified content has an associatednotification based on pre-determined criteria, then deliver thenotification(s) to the user. For example, content is classified as beingrelated to a personal stock of a user and a “ka-ching” sound isdelivered to a user. Content is classified as a exciting sports and aflash of a picture of a baseball over the item is displayed to the user.Content is classified as an exciting event that a user may want toparticipate in and a haptic feedback notification is provided to theuser. In some embodiments, the text on a webpage can be re-written witha preferred font style, color and size so as to accommodate those withpoor eyesight.

FIG. 19 illustrates a table, which illustrates how classified content isdetermined to be displayed to a user. The first column is theclassification of the content. The second column is filtering status.The third column indicates the predetermined notification. For example,in the first row, an item is classified by AI to be in the category of“sale of sporting equipment”, which is filtered and no predeterminednotification is provided. In the second row, an item is classified by AIto be in the category of the category of “news about COVID”, which isnot filtered (and therefore displayed to the user of the my siteapplication) and no predetermined notification is provided. In the thirdrow, an item is classified by AI to be in the category of “news aboutTom Brady”, which is not filtered because the AI algorithm has learnedand classifies items on “Tom Brady” to be “of interest”. Therefore, theAI algorithm does not filter the news about Tom Brady (and thereforedisplays the article to the user). A personalized pre-determinednotification can be generated, such as a exciting, cheering sound from astadium. In the fourth row, an item is classified by AI to be in thecategory of “sale at Talbots”, which is filtered and no predeterminednotification is provided. In the fifth row, an item is classified by AIto be in the category of “MBA recruitment”, which is filtered and nopredetermined notification is provided. In the sixth row, an item isclassified by AI to be in the category of “precious metaladvertisement”, which is filtered and no predetermined notification isprovided. In the seventh row, an item is classified by AI to be in thecategory of “scuba travel”, which is filtered and no predeterminednotification is provided.

In the preferred embodiment, the image displayed on a monitor issegmented into discrete items. Each segmented item is analyzed by anartificial intelligence (AI) algorithm. The AI algorithm performsclassification of each segmented item. The classification is associatedwith a pre-determined filtering status and a pre-determinednotification. In the preferred embodiment, both the filtering status(whether or not an item is filtered/subtracted) and the predeterminednotification (sound, haptic feedback, visual feedback) are determined bythe artificial intelligence algorithm. A user's inputs (e.g., data fromeye tracking as taught in U.S. Ser. No. 16/683,256, A SMART SCROLLINGSYSTEM, which is incorporated by reference in its entirety, data fromuser cursor clicks) are used to train the AI algorithm as to thefiltering status and the predetermined notifications.

The AI filtering system can alleviate a tremendous amount of irritationand concomitant stress. By reducing the vast amount of advertisements,it can dramatically reduce the information overload, which in itself ishighly stressful. In addition, it can also highlight advertisements orinformation that is of moderate to high interest. Overall, this decreasein irritation and only highlighting items of moderately to high interestcan have an overall positive mental health effect.

In addition to filtering out those items of non-interest or irritation,the filtering system can also highlight only those items “of interest”.This way those items of interest will not get buried amongst thenon-interest items and will actually be showcased. In fact, thesepositive items of interest will be a source of happiness and not anirritant and cause stress. This can be done by filtering out items ofnon-interest and only showing items of interest. In addition to onlyshowing these items of interest, there can be a auditory sound (e.g.,jingle) that indicates an interested item. In fact, this jingle can berated from “somewhat interested” to “highly interested” so as to alertthe individual of the relative level of interest for each item. Thejingle can be rated by each individual previously in order to indicatethe level of interest. In the case of a change in the item of interest(new price, new features) it can have a separate jingle, if desired bythe user. The volume can also be adjusted to indicate a level ofimportance to the individual. So if an person, for example, is waitingto hear about a merger, buy out opportunity etc. if can be highlightedby a loud jingle.

If, at any point in time, this process of perusing these items becomesstressful, the my site application/website the AI system will detectthis and automatically reprioritize and decrease the number of hits. Itcan also showcase a previously identified scene of relaxation along withaudio (e.g., sitting on the beach hearing the soothing sounds of thewaves, sitting by a river with birds chirping, looking out at a lake).This AI feature can kick in whenever it senses an individual has reachedtheir limit of frustration with advertisements and frankly with anythingelse.

FIG. 20 illustrates examples of artificial intelligence filteredapplications. A user may have 40 or more applications on their phone.Many applications present extensive information to a user. Some of thisinformation is of non-interest to the user. The my site applicationperforms artificial intelligence filtering of information from theapplications and then presents the information in a consistent formatper user preference, as taught in this patent. A variety of applicationscan use the AI filtering technique as taught in this patent, including,but not limited to, the following: internet searches; e-mail; calendarevents; games; directions; food delivery; and, dating applications.

FIG. 21 illustrates a filtered, AI-personalized search. Processing block2100 illustrates a user entering a search command on the my siteapplication. Processing block 2101 illustrates a search being performedon a search engine (e.g., google.com), which runs in the background.Processing block 2102 illustrates an AI algorithm specific to the useranalyzes the items in the search engine and classifies them (e.g., “ofinterest to user” vs. “of non-interest to user”). Processing block 2103illustrates displaying only items “of interest” to the user on the mysite application. Processing block 2104 illustrates obtaining userpassive and/or active user feedback to re-train the AI algorithm.Processing block 2105 illustrates re-training the AI algorithm based onfeedback from processing block 2104.

FIG. 22 illustrates a filtered, AI-personalized consolidation of emailaccounts. Processing block 2200 illustrates a user entering a searchcommand on the my site application. Processing block 2201 illustrates asearch is performed on a at least one email account (e.g., Gmail), whichruns in the background. The Gmail application is not visualized by theuser. Processing block 2202 illustrates an AI algorithm specific to theuser analyzes the items in the email account(s) and classifies them(e.g., “of interest to user” vs. “of non-interest to user”). Processingblock 2203 illustrates displaying only items “of interest” to the useron the my site application. Processing block 2204 illustrates obtaininguser passive and/or active user feedback to re-train the AI algorithm.Processing block 2205 illustrates re-training the AI algorithm based onfeedback from processing block 2204.

FIG. 23 illustrates utilizing information to enable a psychologicalboost for a user. Processing block 2300 illustrates analyzing an initialset of collected data (e.g., eye movements, facial recognition, voicecharacterization, semantic analysis of user, time of day, exercisepatterns, heart rate, blood pressure) using an AI algorithm. Processingblock 2301 illustrates characterizing the psychological and physicalstate of the user (e.g., happy, sad, motivated, fatigued, etc.).Processing block 2302 illustrates selecting from a group of available AIfriends (an AI algorithm with an assigned name, voice, personality,skills, likes and dislikes). With respect to the processing block 2302,the AI friend could be a person who is known to the user, such as agrandmother. Processing block 2303 illustrates generating a responsefrom said AI friend wherein said response is aimed at generating apsychological boost. Processing block 2304 illustrates collecting userdata (e.g., eye movements, facial recognition, voice characterization,semantic analysis of user, time of day, exercise patterns, heart rate,blood pressure). Processing block 2305 illustrates characterizing thesubsequent psychological and physical state of the user (e.g., happy,sad, motivated, fatigued, etc.) at a subsequent time point. Processingblock 2306 illustrates storing data in processing blocks 2300-2305 in atraining dataset so that the AI algorithm used in step 2303 can learnand improve over time. Processing block 2307 illustrates analyzing theset of collected data in processing block 2304 using an AI algorithm.

FIG. 24 illustrates achieving a psychological boost via a grandmother.

Throughout our lives we form very close ties with certain loved ones.Frequently, these special relationships provide us with a verysupportive and safe space to share and talk through challengingdecisions. We often rely on these special people to listen carefully andempathetically to our concerns and dilemmas and serve as a soundingboard and perhaps even to offer confidential sage advice regardingdecision-making. While the comfort from these special people in ourlives is so important, when they are no longer with us, the grief fromthis loss can be devastating. Wouldn't it be a wonderful comfort to beable to communicate with a special loved one who has passed on? While wecan still pray to them and ask them for guidance and prayers from thehereafter, it might also be a comfort to be able to actually speak to avirtual version of them (e.g., a “virtual grandmother) and hold aconversation with them like you once did. This embodiment provides adevelop a simulated version of a loved one so that these formerconversations could continue.

In addition to being able to speak to a virtual loved one and enjoytheir feedback, conversations and advise, there may be other areas usingthis technology that can be helpful.

First, when remembering ones loved one such as a grandmother, there maybe times when a person longs to say something to that loved one eventhough they have now passed on. Maybe, in such circumstances as thepandemic we are facing today, people have not had the chance to say aproper good-bye or say again how much they loved their special lovedone. We all have moments when we wish we could go back and have saidthings differently. If we were given a chance to redo a conversationalbeit virtually, it is possible this reenactment could perhaps providea sense of closure.

Additionally, there may be other opportunities to redo conversations ordifficult exchanges. As an examples, perhaps one wished that theirparent told them that they were proud of them, but somehow they neverdid. This longing and disappointment may have left a real void and hurt.Perhaps a redo in the exchange with this parent where they said how muchthey were proud of a person albeit a virtual experience would be helpfulto healing some hurts and wounds. People would then at least realizethat had those important words been said in a different universe theywould be happier.

Additionally, there may be still other personal exchanges that a personwished that they had handled differently. Perhaps they were notsensitive to the other person and were not as kind as they might haveintended. They might want a chance to learn from an unsatisfactoryexchange and practice a new strategy and then realize how this newstrategy of alternative comebacks is far more satisfying. Or perhapsthey were not assertive enough and regretted not standing up forthemselves and then had the chance to try out alternative comebacks andsee virtually how much better they felt. These corrective emotionalexperiences can be helpful to our mental well being but, can alsoilluminate and teach us better ways to converse during problematicconversations in the future. Practicing alternative communication withvirtual exchanges with associated AI feedback can be very helpful inadopting improved strategies. Or if someone has anger issues, they cansee how hurtful these behaviors are to others. Virtual training to helpcurtail anger and replace it with more polite and caring responses canalso be practiced virtually. The individual will see the reactions fromthe other person generated by AI when different behaviors aredemonstrated and with practice these will become the automaticresponses.

This figure illustrates several processing blocks to perform thesimulated encounter with the virtual human. For the purpose of teachingthis invention we shall call this person Grandmother (GM). This term isequally applicable to a real grandfather, uncle, brother/sister, orother persons. While the person is alive, encounters with the simulatedloved one could be performed and tested to see how realisticconversation and interactions appeared and updates could be made asneeded. An apparatus, a process and software intended to capture theessence of a real person for posterity's sake.

Processing block 2400 illustrates performing profiling of a human (e.g.,appearance, clothing style, personality, political views, life stories,favorite sports teams, etc.). Many features of this person's life can becategorized and inputted into the profile. Features include, but are notlimited to the following: physical appearance; dress appearance;personality profile; favorite stories; political views; favoriteconversations; favorite sports teams; and, so on. With respect tophysical appearance, a 3D map of the person or person's face could beperformed (e.g., using LIDAR performed under a wide range of facialexpressions including smiling and while talking and while talking).Additionally, video footage of the simulated person including voice,speech patterns, gait patterns, facial gestures will be recorded.

Numerous conversations with this loved one (phone and video) will berecorded. The person could be asked to discuss their opinions on manydiverse issues as well as important values, beliefs and attitudes thathave sustained them in life and they hold dear. They could also be askedto share and discuss a variety of topics such as their favoritememories, activities, books, sports, music, places in the world theyhave visited or wish they had and most admired role models as examples.After numerous video sessions, patterns of their facial expressions,speech patterns (including tone, tempo and word usage) voice patterns(including changes in volume that coincide with changes in affect anddifferent conditions), frequency and patterns of laughter, types andpatterns of gestures, body language (including overall posture as wellas changes that occur when different topics and affect emerge). Duringthese sessions, clothing and hairstyle patterns could be observed andultimately replicated.

A series of assessments would be conducted. These would include variouspersonality tests, which could be used to assess among other variablesspecific needs that the individual has not satisfied and is stillstriving to satisfy. This is important as it is the unsatisfied needsthat direct much one of one's attention, motivation and behavior.Another personality test would involve assessing differences in the waypeople perceive and draw conclusions. Interests tests using Holland'sinstruments could establish an interest profile and concomitantpersonality types. An Intelligence tests could also be employed tomeasure one's reasoning ability and how well one uses information andlogic to draw conclusions and forecast outcomes. Various aptitudemeasures could be considered as well. Profiles could be establishedbased on the various measures and could be used to supplement theformation of an AI simulated version of the loved one. In addition tothe aptitude tests and personality profile, interests, values andbeliefs could also be assessed.

Processing block 2401 illustrates determining a user's interactiontoward a said virtual human. To accomplish this, the user's facialexpressions, tone, word choice, volume, hand gestures, eye contact canall be characterized. This data can be performed on a background of theuser's natural state. For example, if the user is typically monotone involume and becomes somewhat excited in their tone, this needs to betaken into account to fully appreciate the user's current communicationwith the virtual human. Additionally, just as the virtual human ischaracterized, so too should the user's personality profile, views,background, aptitude and other metrics as discussed in processing block2401.

Processing block 2402 illustrates using AI to predict a visual andauditory response of said human (e.g., predict gestures, word choice,volume, tone, etc.). The large amount of training data acquired inprocessing block 2400 is critical in performing this.

Processing block 2403 illustrates generating the visual and auditoryresponse of the GM per processing block 2402. The preferred embodimentis to display the virtual GM on a 3D stereoscopic extended realityheadset or similar headset, which produces 3D visualization of imagery.

In the first implementation, creation of a 3D volume which, in turn,will be the basis for a virtual 3D GM. For example, consider using theprocess outlined in U.S. Pat. No. 8,384,771. There are multipleadditional ways to create a 3D volume. For example, a volume can becreated based on laser distance measurements from the laser source todifferent, closely spaced points on the surface of GM being measured, asperformed in processing block 2400. These measurements could be takenwith associated variations such as but not limited to the following:laser source position relative to the body taking measurements atmultiple angles covering face, upper torso, or full body such that thesecollective laser measurements form a 3D volume; taken with scriptedsequences such as; smile; frown; look concerned; look happy; laughing;etc.; GM read from prepared script and record movements of the mouth,and facial expression changes over time. In some implementations, arecord of body measurements (e.g., height, width, depth over full rangeof body; differences in shoulder width with respect to the head, etc.)In further implementations, the laser points of focus could includevariations of clothing such as on bare arms, bare arms withblouse/shirt; bare arms with sweater. Variations of clothing could alsoinclude but not be limited to: what one wears to a sporting event; aparty, at home in casual attire. Based on these variations of the GMposture, a multiplicity of virtual 3D volumes would be created.

In the second implementation, videos would be created to obtain facialexpressions, movement of the head and arms, etc., during variousactivities. These activities could include but not be limited to thefollowing: taken with scripted facial sequences such as: smile; frown;blinking; look concerned; look happy; laughing; etc.; reading from a GMselected script; performing household duties; walking in a park;sitting/standing in various rooms of GM living location. In someimplementations, interactive scenarios could be prepared and rehearsedso that changes in facial expressions over time to reflect GM'sreactions to differing topics. In some implementations, mannerisms suchas, but not limited to, how GM nods her head, laughs, actions of movinghand and arms would be video taped. In some implementations, during therecording of the videos, color would be used in the recording processfor different skin tones. Based on the setting (s) there may be shadowsand bright areas indicative of the effects of different lighting. Notethat U.S. Pat. No. 8,384,771 allowed for voxels of different colors andshadings, so this can be used for rendering on the extended realitydisplay. In some implementations, use different elements of wardrobe toencompass what GM would wear under differing outside/event conditions.For example, dress up for Thanksgiving dinner; going to 4th of Julyparty; normal day at home for the different seasons. Note: these outergarments could correspond those garments used the above measurements. Infurther implementation of this videotaping, a careful review of coloringand proper shades would be performed such that the resulting 3D virtualGM closely matched that of the video taped GM.

In the third implementation, personality tests would be administered toGM These personality traits would subsequently be used in conjunctionwith artificial intelligence (AI) element to formulate GM responsesduring interactions with persons using the GM system. As an example,have each participant (i.e., GM (mandatory), expected high frequencyusers such as siblings, sons and daughters, grandchildren, key friends)to take the Myers Briggs (MB) personality test. Find out the generalpersonality characteristics such as whether GM is an introvert orextravert, a person who is sensing or intuitive, thinking or feeling,and judging or perceiving. How does GM best interact with each of thepersonality categories? Next, are there any past interactions that wouldhave a bearing on future GM interactions (e.g., with specificindividuals, a past history of travel together, calamities that may havehappened together, nicknames/greeting between GM and other highlikelihood users)? What are the relationships between GM and other highlikelihood users and, importantly, the relationships between the varioushigh likelihood users? What is meta data regarding high likelihood userssuch as but not limited to the following: birthdates/birthdays;educational history, work experience; and key interests. The AI elementwould also include a set of key phrases, which if used during aninteraction, would trigger a response in facial expression of GM andalso a change of mood (described in a subsequent implementation). Insome implementations voice tone analysis could be utilized to betterunderstand the actual emotional state of the person interacting with GM.For example, the person might say I'm OK but voice analysis mightindicate the person is actually troubled. The personality tests, pastinteractions, relationships and meta data would be integrated into acoherent AI response/interactions package specific to GM. In somefurther implementations this would include development of a set of rulesfor interactions between GM and each of the high likelihood users.

In the next implementation, cause the 3D virtual volume of GM totransition in a natural way during the course of interactions. Thiswould be accomplished through deformations of portions of the 3D volumecreated in the initial implementation using the processes described inU.S. Pat. Nos. 10,878,639 and 10,950,338. and also with directionsprovided by the AI element. For example, if GM was greeting one of thegrandchildren, the AI element in conjunction with deformations wouldmodify a normal the facial expressions to an expression of a smile andthe corresponding voxels would change accordingly. In contrast, when AIelement determines that there has been a cause for concern expressed bykey words/phrases uttered by the person interacting with GM, theexpression from normal to concerned look would take place throughdeformations. In further implementations, multiple interaction scenarioscould be prepared for interactions between GM and each of the highlikelihood users. The manner in which the voxel manipulation changed thenormal expression could be compared with the video tapes taken in thesecond implementation. If discrepancies exist, the expression(s) wouldbe altered accordingly.

In the next implementation, cause the 3D virtual volume of GM to besuperimposed onto a virtual mannequin (i.e., the virtual GM wouldreplace the corresponding elements of the virtual mannequin—head forhead, hair for hair, arms and hands for arms and hands, and so on). Inthe first step in this implementation, the virtual mannequin would bemodified (e.g., stretched or shrunk in accordance with GM's age group,body size and shape). Mannequin appendages such as arms and legs wouldbe modified to correspond to measured appendages of GM. In someimplementations, use different elements of GM's wardrobe to encompasswhat GM would wear under differing outside/event conditions would adornthe mannequin and its appendages.

In the next implementation, cause the 3D virtual volume of GM, which hasnow become the virtual GM mannequin to be able to employ articulationsaspects. These articulations would include but are not limited to thefollowing: head movements when nodding approval and when laughing; armsand hand movements when clapping or giving a ‘high five’; performinghousehold tasks; working on a GM hobby; walking in a park. (Note: thescope of these activities determined by those persons directingpreparation of the virtual GM).

In the next implementation, cause the 3D virtual volume of GM, which hasnow become the virtual GM mannequin to interact with and respond to thecommands of the AI element. The AI element would first, inter alia:evaluate the current situation between GM and the particular highlikelihood including factors such as the situation at hand, mood of theinteracting parties, particular setting during discussions. The AIelement would next, inter alia: determine the appropriate GM response toinclude but be limited to the following: verbal response and tone ofdelivery; modifications, as required, in facial expressions; associatedarticulations of head and appendages. The AI element would next, interalia: initiate the GM response including verbal, facial expressions withassociated deformations, and associated mannequin articulations. Notethat this is an ongoing sequences of queries and responses on the GM'spart: ponder questions based on known situations; listen and evaluatethe situation as it evolves during the interaction; prepare and deliverresponses based on the interactions; update the memory property based onthe interactions. The net result would be appropriate, timelyinteractions between GM and each of the high likelihood users.

In the next implementation, prepare interface dynamics with the intentthat GM responds in an appropriate manner consistent with that of thePerson Being Interacted With (PBIW). Examples include but are notlimited to: 1) GM should try to match the mood of the PBIW: if the PBIWis happy, GM should be happy; if the PBIW is sad, likewise GM should besad; if the PBIW is pensive, so should be GM; if the PWIB is concerned,GM should be also; and so on. 2) GM should be responsive to the attitudeof the PBIW: if the PBIW is positive, GM should be positive also; if thePBIW has a negative attitude, GM may be positive as a means ofencouragement. 3) GM should be responsive to the energy level of thePBIW: if the PBIW is apathetic, GM may be bright and cheerful.

In some implementations, the interactions will be built around a familyand friends collectively watching in 3D, listening to and interactingwith the virtual GM. GM could initially provide her background whichcould, inter alia, include: life in early, mid, and later life;schooling; ancestral linkages; key events over different stages of life;fun tales associated with the high likelihood family and friendsattendees; a question and answer period.

In some embodiments, a diversity of locations for recording GM isperformed. Sometimes in living room, kitchen, garden, park, etc.Sometimes in differing lighting conditions. Sometimes feedback into AIfor initializing interactions is incorporated.

Some embodiments comprise setting a tone of GM appearance to match thosein video to ensure authenticity of visuals. This will help achieve thebest match of voxel tone with tone of corresponding video pixel

Additionally, AI is important for interactions. AI response would takeinto account, inter alia, the factors cited in step 10, below.

Processing block 2404 illustrates recording the response of the user(e.g., facial expressions, response with words, user heart rate, userblood pressure, user eye movements, and other available data).

Processing block 2405 illustrates evaluating the interaction (e.g.,mental health assessment,

Processing blocks 2404 and 2405 are critical so that the AI can learn tomodify reactions over time to continuously improve even after data inprocessing block 2400 is acquired.

Processing block 2406 illustrates storing the interaction.

In the next implementation, cause the 3D virtual volume of GM, which hasnow become the virtual GM mannequin to have a memory property integralto the response of the GM with each particular high likelihood user.This response is dynamic AI which would evolve over time based onfactors such as but not limited to the following: last and previousinteractions to formulate comprehensive understanding of user conditionwith respect to but, not limited to the following: friends; love life;job; health; financial status as of last interface. In someimplementations, this information will be integrated into AI toformulate questions by GM of person interacted with such as: ‘how isyour friend X or Y’? How is you job with Company X going? You have abirthday coming in two weeks—what would you like for a present? Asresponses are received and new information is disclosed by personinteracting with GM, the AI element is continuously updated. An updateof the AI element will be performed upon conclusion of the interactionbased of this latest interface.

FIG. 25 illustrates a method and apparatus for a dynamic personalizedwebpage. 2500 illustrates connecting to the Internet. 2501 illustratesusing a first webpage wherein said first webpage is loaded, and whereinsaid first webpage is not displayed to a user. 2502 illustrates using asecond webpage wherein said second webpage is loaded, and wherein saidsecond webpage is not displayed to a user. 2503 illustrates using acomputer algorithm to label a first item from said first webpage as “ofinterest”. It should be noted that there are a group of items in thefirst webpage which can be classified. For example, there may be 10items on the first webpage and only one of them may be labeled “ofinterest”. It should be noted that multiple “of interest” items can belabeled form the first webpage or the links within the first webpage.2504 illustrates extracting said first item from said first webpage. Itshould be noted that multiple “of interest” items can be extracted formthe first webpage or the links within the first webpage. 2505illustrates using said computer algorithm to label a second item fromsaid second webpage as “of interest”. It should be noted that there area group of items in the second webpage which can be classified. Forexample, there may be 30 items on the second webpage and only one ofthem may be labeled “of interest”. It should be noted that multiple “ofinterest” items can be labeled form the second webpage or the linkswithin the second webpage. 2506 illustrates extracting said second itemfrom said second webpage. It should be noted that multiple “of interest”items can be extracted form the second webpage or the links within thesecond webpage. 2507 illustrates generating said dynamic personalizedwebpage wherein said dynamic personalized webpage comprises said firstitem and said second item. It should be noted that multiple “ofinterest” items form the first webpage or the links within the firstwebpage or from the second webpage or the links within the secondwebpage. 2508 illustrates displaying said dynamic personalized webpageto said user.

FIG. 26 illustrates options for the dynamic personalized webpage. Theartificial intelligence algorithm can learn based on user feedback.Also, it can improve classification accuracy of “of interest” based onat least one of the group consisting of: an amount of time spent by saiduser on said first item or said second item; whether said user clicks onsaid first item or said second item; and, whether said user sends saidfirst item or said second item to another person. Some of the websiteoptions include category grouping. For example, grouping into categoriescan facilitate easy search via user input (e.g., CNN.com and FoxNews.comare grouped into the “News” category, Disney.com and Netflix.com aregrouped into the “Entertainment” category). Next, user input options:click with mouse, tap with finger, eye tracking, etc. Next, updates ofthe dynamic personalized webpage can be performed in near real time. Forexample, updates could be performed every one minute. Some embodimentscould be more frequent and some embodiments could be less frequent.Also, hyperlinks within websites can also be searched for “of interest”content and such classified content can be extracted and displayed.Also, the number of webpages used can also vary. Typically, there willbe at least two, but it can be three, four or even more than fourwebsites. With respect to the presentation, the preferred embodiment isfor the majority of the content within the loaded, hidden webpages to befiltered. In other words, it would be better to err on the side ofclassifying an item as “of non-interest” rather than classifying an itemas “of interest”. Some embodiments comprise options for personalizedbackground, font options (size, color, type), image options(corrections, color, artistic effects). Some embodiments comprise usingpredetermined notifications based on classification of said content(e.g., sound notification, haptic notification, visual notification).Some embodiments comprise wherein a user has the option to placeadvertisements on the dynamic personalized webpage to secure a revenuestream wherein at least some of the revenue from the advertisements goesto the user.

Throughout the entirety of the present disclosure, use of the articles“a” or “an’ to modify a noun may be understood to be used forconvenience and to include one, or more than one of the modified noun,unless otherwise specifically stated. Elements, components, modules,and/or parts thereof that are described and/or otherwise portrayedthrough the figures to communicate with, be associated with, and/or bebased on, Something else, may be understood to so communicate, beassociated with, and or be based on in a direct and/or indirect manner,unless otherwise stipulated herein. The device(s) or computer systemsthat integrate with the processor(s) may include, for example, apersonal computer(s), workstation(s) (e.g., Sun, HP), personal digitalassistant(s) (PDA(s)), handheld device(s) such as cellular telephone(s),laptop(s), handheld computer(s), or another device(s) capable of beingintegrated with a processor(s) that may operate as provided herein.Accordingly, the devices provided herein are not exhaustive and areprovided for illustration and not limitation. References to “amicroprocessor and “a processor, or “the microprocessor and “theprocessor.” may be understood to include one or more microprocessorsthat may communicate in a stand-alone and/or a distributedenvironment(s), and may thus be configured to communicate via wired orwireless communications with other processors, where such one or moreprocessor may be configured to operate on one or moreprocessor-controlled devices that may be similar or different devices.Use of such “microprocessor or “processor terminology may thus also beunderstood to include a central processing unit, an arithmetic logicunit, an application-specific integrated circuit (IC), and/or a taskengine, with such examples provided for illustration and not limitation.Furthermore, references to memory, unless otherwise specified, mayinclude one or more processor-readable and accessible memory elementsand/or components that may be internal to the processor-controlleddevice, external to the processor-controlled device, and/or may beaccessed via a wired or wireless network using a variety ofcommunications protocols, and unless otherwise specified, may bearranged to include a combination of external and internal memorydevices, where Such memory may be contiguous and/or partitioned based onthe application. Accordingly, references to a database may be understoodto include one or more memory associations, where such references mayinclude commercially available database products (e.g., SQL, Informix,Oracle) and also include proprietary databases, and may also includeother structures for associating memory Such as links, queues, graphs,trees, with such structures provided for illustration and notlimitation. References to a network, unless provided otherwise, mayinclude one or more intranets and/or the Internet, as well as a virtualnetwork. References hereinto microprocessor instructions ormicroprocessor-executable instructions, in accordance with the above,may be understood to include programmable hardware.

Unless otherwise stated, use of the word “substantially’ may beconstrued to include a precise relationship, condition, arrangement,orientation, and/or other characteristic, and deviations thereof asunderstood by one of ordinary skill in the art, to the extent that suchdeviations do not materially affect the disclosed methods and systems.Throughout the entirety of the present disclosure, use of the articles“a” or “an’ to modify a noun may be understood to be used forconvenience and to include one, or more than one of the modified noun,unless otherwise specifically stated. Elements, components, modules,and/or parts thereof that are described and/or otherwise portrayedthrough the figures to communicate with, be associated with, and/or bebased on, Something else, may be understood to so communicate, beassociated with, and or be based on in a direct and/or indirect manner,unless otherwise stipulated herein. Although the methods and systemshave been described relative to a specific embodiment thereof, they arenot so limited. Obviously many modifications and variations may becomeapparent in light of the above teachings. Many additional changes in thedetails, materials, and arrangement of parts, herein described andillustrated, may be made by those skilled in the art. Having describedpreferred embodiments of the invention it will now become apparent tothose of ordinary skill in the art that other embodiments incorporatingthese concepts may be used. Additionally, the software included as partof the invention may be embodied in a computer program product thatincludes a computer useable medium. For example, such a computer usablemedium can include a readable memory device, such as a hard drivedevice, a CD-ROM, a DVD ROM, or a computer diskette, having computerreadable program code segments stored thereon. The computer readablemedium can also include a communications link, either optical, wired, orwireless, having program code segments carried thereon as digital oranalog signals. Accordingly, it is submitted that that the inventionshould not be limited to the described embodiments but rather should belimited only by the spirit and scope of the appended claims.

Several features, aspects, embodiments and implementations have beendescribed. Nevertheless, it will be understood that a wide variety ofmodifications and combinations may be made without departing from thescope of the inventive concepts described herein. Accordingly, thosemodifications and combinations are within the scope of the followingclaims.

What is claimed is:
 1. A method of using a processor to generate adynamic personalized webpage comprising: using a first webpage whereinsaid first webpage is loaded, wherein said first webpage containsmultiple items, and wherein said first webpage is not displayed to auser; using a second webpage wherein said second webpage is loaded,wherein said second webpage contains multiple items, and wherein saidsecond webpage is not displayed to said user; using a computer algorithmto label a first item from said first webpage as “of interest” whereinsaid first item comprises a first hyperlink; extracting said first itemfrom said first webpage; using said computer algorithm to label a seconditem from said second webpage as “of interest”; extracting said seconditem from said second webpage; generating said dynamic personalizedwebpage wherein said dynamic personalized webpage comprises said firstitem and said second item; using a personalized display for said dynamicpersonalized webpage comprising changing a font setting of said firstitem or said second item to a user preferred font setting; using ahaptic notification to said user based on a classification of content;displaying said dynamic personalized webpage to said user; wherein whensaid user clicks on said first hyperlink, a webpage corresponding tosaid first hyperlink is loaded and said webpage corresponding to saidfirst hyperlink is not displayed to said user; using said computeralgorithm to label an item from said webpage corresponding to said firsthyperlink as “of interest”; extracting said item from said webpagecorresponding to said first hyperlink; modifying said dynamicpersonalized webpage by incorporating said item from said webpagecorresponding to said first hyperlink to generate a modified dynamicpersonalized webpage; and displaying said modified dynamic personalizedwebpage to said user.
 2. The method of claim 1 further comprising:wherein said computer algorithm comprises an artificial intelligencealgorithm; and wherein said artificial intelligence algorithm learnsbased on feedback from said user.
 3. The method of claim 2 furthercomprising wherein said artificial intelligence algorithm improvesclassification accuracy of “of interest” based on at least one of thegroup consisting of: an amount of time spent by said user on said firstitem or said second item; whether said user clicks on said first item orsaid second item; and whether said user sends said first item or saidsecond item to another person.
 4. The method of claim 1 furthercomprising: using a third webpage wherein said third webpage is loaded,wherein said third webpage contains multiple items, and wherein saidthird webpage is not displayed to a user; using a fourth webpage whereinsaid fourth webpage is loaded, wherein said fourth webpage containsmultiple items, and wherein said fourth webpage is not displayed to saiduser; using said computer algorithm to label a third item from saidthird webpage as “of interest”; extracting said third item from saidthird webpage; using said computer algorithm to label a fourth item fromsaid fourth webpage as “of interest”; extracting said fourth item fromsaid fourth webpage; and generating a second dynamic personalizedwebpage wherein said second dynamic personalized webpage comprises saidthird item and said fourth item.
 5. The method of claim 4 furthercomprising: wherein said first webpage and said second webpage areassigned to a first category; and wherein a third webpage and a fourthwebpage are assigned to a second category.
 6. The method of claim 5further comprising: when said user clicks on a button corresponding tosaid first category, said dynamic personalized webpage comprising saidfirst item and said second item is displayed to said user; and when saiduser clicks on a button corresponding to said second category, saiddynamic personalized webpage comprising said third item and said fourthitem is displayed to said user.
 7. The method of claim 1 furthercomprising wherein said dynamic personalized webpage comprisesadvertisements.
 8. The method of claim 7 further comprising wherein atleast some revenue from advertisers is paid to said user.
 9. The methodof claim 1 further comprising updating said dynamic personalized webpagebased on updates from said first webpage and said second webpage. 10.The method of claim 1 further comprising displaying a predeterminednotification to said user based on a classification of content whereinsaid predetermined notification is a sound notification.
 11. The methodof claim 1 further comprising displaying a predetermined notification tosaid user based on a classification of content wherein saidpredetermined notification is a visual notification.
 12. The method ofclaim 1 further comprising wherein said font setting comprises at leastone of the group consisting of: a font type; a font size; and a fontcolor.
 13. The method of claim 1 further comprising using a personalizeddisplay for said dynamic personalized webpage comprising changing imagesize of said first item or said second item to a user preferred imagesize.
 14. A computer system comprising: a memory; a processor; adisplay; a communications interface; an interconnection mechanismcoupling the memory, the processor, the display and the communicationsinterface; and wherein the memory is encoded with an application togenerate a dynamic personalized webpage that when performed on theprocessor, provides a process for processing information, the processcausing the computer system to perform the operations of: using a firstwebpage wherein said first webpage is loaded, wherein said first webpagecontains multiple items, and wherein said first webpage is not displayedto a user; using a second webpage wherein said second webpage is loaded,wherein said second webpage contains multiple items, and wherein saidsecond webpage is not displayed to said user; using a computer algorithmto label a first item from said first webpage as “of interest” whereinsaid first item comprises a first hyperlink; extracting said first itemfrom said first webpage; using said computer algorithm to label a seconditem from said second webpage as “of interest”; extracting said seconditem from said second webpage; generating said dynamic personalizedwebpage wherein said dynamic personalized webpage comprises said firstitem and said second item; using a personalized display for said dynamicpersonalized webpage comprising changing a font setting of said firstitem or said second item to a user preferred font setting; using ahaptic notification to said user based on a classification of content;displaying said dynamic personalized webpage to said user; modifyingsaid dynamic personalized webpage comprising; wherein when said userclicks on said first hyperlink, a webpage corresponding to said firsthyperlink is loaded and said webpage corresponding to said firsthyperlink is not displayed to said user; using said computer algorithmto label an item from said webpage corresponding to said first hyperlinkas “of interest”; extracting said item from said webpage correspondingto said first hyperlink; and incorporating said item from said webpagecorresponding to said first hyperlink into said dynamic personalizedwebpage to generate a modified dynamic personalized webpage; anddisplaying said modified dynamic personalized webpage to said user. 15.A non-transitory computer readable medium having computer readable codethereon for generating a dynamic personalized webpage, the mediumcomprising: instructions for using a first webpage wherein said firstwebpage is loaded, wherein said first webpage contains multiple items,and wherein said first webpage is not displayed to a user; instructionsfor using a second webpage wherein said second webpage is loaded,wherein said second webpage contains multiple items, and wherein saidsecond webpage is not displayed to said user; instructions for using acomputer algorithm to label a first item from said first webpage as “ofinterest” wherein said first item comprises a first hyperlink;instructions for extracting said first item from said first webpage;instructions for using said computer algorithm to label a second itemfrom said second webpage as “of interest”; instructions for extractingsaid second item from said second webpage; instructions for generatingsaid dynamic personalized webpage wherein said dynamic personalizedwebpage comprises said first item and said second item; instructions forusing a personalized display for said dynamic personalized webpagecomprising changing a font setting of said first item or said seconditem to a user preferred font setting; instructions for using a hapticnotification to said user based on a classification of content;instructions for displaying said dynamic personalized webpage to saiduser instructions for modifying said dynamic personalized webpagecomprising: wherein when said user clicks on said first hyperlink, awebpage corresponding to said first hyperlink is loaded and said webpagecorresponding to said first hyperlink is not displayed to said user;using said computer algorithm to label an item from said webpagecorresponding to said first hyperlink as “of interest”; extracting saiditem from said webpage corresponding to said first hyperlink; andincorporating said item from said webpage corresponding to said firsthyperlink into said dynamic personalized webpage to generate a modifieddynamic personalized webpage; and instruction for displaying saidmodified dynamic personalized webpage to said user.